Python Data Cleaning Cookbook E-Book

Michael Walker

• Herausgeber: Packt Publishing
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 584

Veröffentlichungsjahr: 2024

Beschreibung

Jumping into data analysis without proper data cleaning will certainly lead to incorrect results. The Python Data Cleaning Cookbook - Second Edition will show you tools and techniques for cleaning and handling data with Python for better outcomes.

Fully updated to the latest version of Python and all relevant tools, this book will teach you how to manipulate and clean data to get it into a useful form. he current edition focuses on advanced techniques like machine learning and AI-specific approaches and tools for data cleaning along with the conventional ones. The book also delves into tips and techniques to process and clean data for ML, AI, and NLP models. You will learn how to filter and summarize data to gain insights and better understand what makes sense and what does not, along with discovering how to operate on data to address the issues you've identified. Next, you’ll cover recipes for using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors and generate visualizations for exploratory data analysis (EDA) to identify unexpected values. Finally, you’ll build functions and classes that you can reuse without modification when you have new data.

By the end of this Data Cleaning book, you'll know how to clean data and diagnose problems within it.

Leseprobe

Python Data Cleaning Cookbook

Second Edition

Prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn, and OpenAI

Michael Walker

Python Data Cleaning Cookbook

Second Edition

Copyright © 2024 Packt Publishing

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Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Senior Publishing Product Manager: Gebin George

Acquisition Editor – Peer Reviews: Gaurav Gavas

Project Editor: Namrata Katare

Content Development Editor: Deepayan Bhattacharjee

Copy Editor: Safis Editing

Technical Editor: Karan Sonawane

Proofreader: Safis Editing

Indexer: Subalakshmi Govindhan

Presentation Designer: Ajay Patule

Developer Relations Marketing Executive: Vignesh Raju

First published: December 2020

Second edition: May 2024

Production reference: 1230524

Published by Packt Publishing Ltd.

Grosvenor House

11 St Paul’s Square

Birmingham

B3 1RB, UK.

ISBN: 978-1-80323-987-3

www.packt.com

Contributors

About the author

Michael Walker has worked as a data analyst for 37 years at various educational institutions. He has also taught data science, research methods, statistics, and computer programming to undergraduates since 2006. He is currently the Chief Information Officer at College Unbound in Providence, Rhode Island. Michael is also the author of Data Cleaning and Exploration with Machine Learning.

I deeply appreciate the editors of this text, particularly Namrata Katare and Deepayan Bhattacharjee, and the technical reviewer, Kalyana Bedhu. They made this text so much better than it would have been otherwise. Any remaining issues are completely the fault of the author. I would also like to thank my partner over the last several decades, Karen Walker. She has had to listen to me go on and on about statistics and data, and the teaching of both topics, for all those years. Along the way, she patiently and lovingly taught me most of what I know about how to teach and be in community with other humans, even when that means putting aside some modeling or programming task for a few hours to focus on what matters most. For the sake of the reader, I hope this book reflects some fraction of her thoughtfulness and patience.

About the reviewer

Kalyana Bedhu, a recipient of patents and an author of award-winning papers and data science courses, is an Engineering Leader and Architect of Enterprise AI/ML at Fannie Mae. He has over 20 years of experience in applied AI/ML across various companies like Microsoft, Ericsson, Sony, Bosch, Fidelity, and Oracle. As a practitioner of Data Science at Ericsson, he helped set up a data science lab. He played a pivotal role in transforming a central IT organization into an AI and data science engine.

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